In this episode, we explore how Netflix enhanced recommendation personalization using foundation models. These models can process massive user histories through tokenization and attention mechanisms, while also addressing the cold-start problem with hybrid embeddings. The work highlights how principles from large language models can be adapted to build more effective recommendation systems at scale.

For more details, you can refer to their published tech blog, linked here for your reference: https://netflixtechblog.com/foundation-model-for-personalized-recommendation-1a0bd8e02d39